In this paper, we propose to improve entity linking by capturing latent entity type information with BERT. Firstly, we build entity embeddings from BERT by averaging all the context representation extracted from pre-trained BERT. Then we integrate a BERT-based entity similarity into the local model of the state-of-the-art method by [1]}. The experiment results show that our model significantly outperforms the baseline with an absolute improvement of 1.32% F1 on in-domain AIDA-CoNLL test set and average 0.80% F1 on five out-domain test datasets. The detailed experiment analysis shows that our method corrects most of the type errors produced by the baseline. In the future, we would like to design global modeling methods which can take advantage of the BERT architecture and investigate other ways to use the prior feature.
